Yuan Shaoxuan, Zhu Zhiwen, Lu Jiayi, Zheng Fengru, Jiang Hao, Sun Qiang
Materials Genome Institute, Shanghai University, Shanghai 200444, China.
Molecules. 2023 Jul 13;28(14):5387. doi: 10.3390/molecules28145387.
Scanning tunneling microscopy (STM) imaging has been routinely applied in studying surface nanostructures owing to its capability of acquiring high-resolution molecule-level images of surface nanostructures. However, the image analysis still heavily relies on manual analysis, which is often laborious and lacks uniform criteria. Recently, machine learning has emerged as a powerful tool in material science research for the automatic analysis and processing of image data. In this paper, we propose a method for analyzing molecular STM images using computer vision techniques. We develop a lightweight deep learning framework based on the YOLO algorithm by labeling molecules with its keypoints. Our framework achieves high efficiency while maintaining accuracy, enabling the recognitions of molecules and further statistical analysis. In addition, the usefulness of this model is exemplified by exploring the length of polyphenylene chains fabricated from on-surface synthesis. We foresee that computer vision methods will be frequently used in analyzing image data in the field of surface chemistry.
扫描隧道显微镜（STM）成像因其能够获取表面纳米结构的高分辨率分子级图像，已被常规应用于研究表面纳米结构。然而，图像分析仍严重依赖人工分析，这通常很费力且缺乏统一标准。最近，机器学习已成为材料科学研究中用于图像数据自动分析和处理的强大工具。在本文中，我们提出了一种使用计算机视觉技术分析分子STM图像的方法。我们通过用关键点标记分子，基于YOLO算法开发了一个轻量级深度学习框架。我们的框架在保持准确性的同时实现了高效率，能够识别分子并进行进一步的统计分析。此外，通过探索由表面合成制备的聚苯撑链的长度，例证了该模型的实用性。我们预见计算机视觉方法将经常用于分析表面化学领域的图像数据。
